Space-time modeling of cascading hazards: Chaining wildfires, rainfall and landslide events through machine learning

The current study sets out to explore yearly landslide susceptibility dynamics on slopes regularly affected by fires. To do so, two yearly inventories have been generated, one for the landslides and one for the wildfires, for an area of approximately 2 km² and for a period of 24 years. It is important to stress that space–time data-driven models employed for susceptibility assessment are relatively new, and their application so far has mostly linked landslide occurrences to the precipitation trigger and the standard morphometric characteristics of the landscape at hand. Here we also consider an additional element of disturbance to the slope equilibrium, in the form of burnt areas tested from one up to three years priors to the reference landslide occurrence time. The relevance of the wildfire spatiotemporal signal is tested as part of a multi-variate modeling procedure. The results highlight at least a 10 % performance increase when these wildfire-related predictors are featured (from an average AUC of 0.75 to 0.85 in a random forest modeling framework). The associated yearly variations in the landslide occurrence probability are translated into individual maps, stressing the extent to which the standard and static definition of susceptibility does not hold especially in the context of multiple hazards and in an urban setting such as the Camaldoli hills (Naples, Italy) we chose for our test site.